Available via license: CC BY
Content may be subject to copyright.
OriginalResearch
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 3
DeterminingtheUniversity’sPositionina
Multi‐stakeholderCollaborativeNetwork
TracieEvansReding
1
andKristinVanWyngaarden
1
1
DepartmentofTeacherEducation,UniversityofNebraskaatOmaha
Citeas:EvansReding,T.&VanWyngaarden,K.(2019).DeterminingtheUniversity’sPositioninaMulti‐stakeholder
CollaborativeNetwork.MetropolitanUniversities,31(1),3‐23.DOI:10.18060/22604
ThisisanopenaccessarticledistributedunderthetermsoftheCreativeCommonsAttributionLicense.
Editor:ValerieL.Holton,Ph.D.
Abstract
Complexproblemsarebeingapproachedthroughcollaborationsthatcrosssectorsincluding
businesses,nonprofits,publicinstitutions,andacademia.SocialNetworkAnalysis(SNA)
methodshavebeenadoptedtohelpmanagetheselargecollaborations,anditisusefulnotonly
forexploringthenetworkdynamicsofthecollaborationasawhole,butalsoforexploring
whereanindividualorganizationlieswithinthenetwork.Universitiescanbenefitfrom
understandingtheirpositionandtieswithinanetworkandutilizethatinformationto
strengthentheirpositionwithinthesecollaborationswhilefosteringcollaborationswithinthe
network.ThisstudyappliedSNAtodeterminetheinfluentialpositionofanurbanuniversity
withinamulti‐stakeholdercollaborativenetwork(MSCN).Theuniversityinthisstudyholds
moreformalintra‐sectorrelationshipsandmoreinformalinter‐sectorrelationshipswiththe
organizationtypesintheMSCN.Thefindingsalsoshowthattheuniversitydoesholda
prominentpositionwithintheinformalnetworkoftheMSCN;however,itdoesnotholda
positionofprominencewithintheformalnetworkoftheMSCN.Fosteringtheseformaland
informalrelationshipswouldallowtheuniversitytostrategicallypromotebeneficial
collaborationsfortheuniversityandthenetworkasawhole.
Keywords:collaborationmanagement,cross‐sectorcollaborations,socialnetworkanalysis,
socialcapital
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 4
Introduction
Collaborations among stakeholders from private, nonprofit, and public sectors that focus on
addressing complex problems through the innovative combination of their knowledge and
resources have become increasingly popular (Becker & Smith, 2018). These collaborations have
been referred to as multi-stakeholder collaborative networks (MSCNs), and within MSCNs,
universities are a key element because they produce human capital and contribute to innovation
production through the development of new knowledge (Etzkowitz et al., 2000). Universities
have also been shown to develop innovation networks within their communities (Benneworth &
Hospers, 2007). One increasingly complex issue that requires the development of innovation
networks to target is the Science, Technology, Engineering, and Mathematics (STEM) workforce
shortage. Developing local and regional STEM Learning Ecosystems has been identified as one
of the solutions. STEM Learning Ecosystems are localized MSCNs that are “dynamic
collaborations among school, out-of-school time programs, STEM expert institutions (such as
museums, science centers, institutions of higher education, and STEM professional associations),
the private sector, community-based organizations, youth and families” (STEM Ecosystems,
2018). STEM Learning Ecosystems aim to provide the structure through which high-quality and
cross-sector learning is available to all learners, resulting in the development of important skills
and engagement within STEM disciplines necessary to develop and nourish the STEM
workforce (STEM Ecosystems, 2018).
There are currently 84 STEM Learning Ecosystems that comprise the global STEM Ecosystems
Community of Practice, including the Omaha STEM Ecosystem (OSE), which serves the
Omaha, NE metropolitan area. The member organizations of the OSE comprise a variety of
stakeholder groups including multinational companies, educational institutions, non-profit
organizations, and science centers and museums. Within the OSE, the University of Nebraska at
Omaha (UNO) has taken a large leadership role as one of the two founding institutions, as well
as maintaining a strong presence on the OSE Founders board and the OSE Steering Committee.
One of the guiding principles of the OSE is to:
Sustain an overarching structure to bring community partners together to advance STEM
learning as a priority in Omaha. We welcome diverse partners and experiment with
innovative ways for engagement. By creating a network of community organizations, we
provide a venue for collaboration around solutions to STEM issues and leveraging of
resources (OSE Steering Committee, 2017).
This guiding principle clearly outlines the intention of the OSE to develop and foster an
innovative and capacity building network to address the STEM workforce shortage in the Omaha
metropolitan area. UNO’s intense involvement in the OSE provides ample opportunity for it to
help manage this collaborative network and its own network position. In order to do this, UNO
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 5
must evaluate the overall network structure in order to understand the existing ties of the overall
network, as well as UNO-specific ties, in order to improve network innovation. With this
knowledge, UNO can help the OSE focus efforts in order to maintain current ties while also
facilitating the development of new ones.
In order to focus the efforts of a MSCN, such as the OSE, the network itself must be evaluated,
which may be done through Social Network Analysis (SNA) in order to improve collaboration
management. SNA is the methodology utilized to examine formal and informal relationships by
investigating relational concepts, processes, and patterns within a social network. A social
network is multi-faceted and may be described as a business, university, school, neighborhood,
organization, or even a family. The participants in these networks are known as actors, and it is
their interactions and relationships, known as ties, that are examined within SNA studies
(Borgatti & Ofem, 2010; Kenis & Oerlemans, 2007; Wasserman & Faust, 1994).
The purpose of this article is to present a method employed by UNO and the OSE, which
evaluates the development of its network by examining the presence and absence of ties,
individual actor positions, and whole network metrics. SNA has been used by the OSE to
evaluate its network and make strategic plans based on the data.
SocialNetworkAnalysis,SocialCapital,andInnovation
As previously mentioned, MSCNs, such as the OSE, depend on the relationships present
amongst the members, and many collaborations are recognizing the importance to analyze these
relationships using SNA for management purposes. Social Capital (SC) has been identified as a
popular theory used within SNA studies to help when managing innovation in collaborations. SC
is often referred to as the actual and potential resources within a network because of the
relationships present (Nahapiet & Ghoshal, 1998). Various dimensions of SC can be targeted;
however, this study focuses on relational SC and structural SC. Relational SC refers to the
underlying nature of the ties that are present amongst the actors within a network and is
measured through network density, which represents the number of ties present as compared to
the number of ties possible. Relational SC results from a history of interactions and has been
shown to result in trust and the development of norms in cross-sector alliances, which aids in the
flow of knowledge and willingness to collaborate on innovations (Vlaisavljevic, Cabello‐
Medina, & Pérez‐Luño, 2016; Zach & Hill, 2017). The majority of research dedicated to the role
of relational SC with regards to innovation has focused on analysis and development efforts
within the pharmaceutical and technology sectors; researchers have found no existing
scholarship that focuses on the relational SC of MSCNs, such as STEM learning ecosystems.
Due to the diverse nature of stakeholders within a MSCN, it is expected that certain organization
types will bring varying degrees of relational SC with one another due to previous
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 6
collaborations, and typically, organizations within the same sectors seem to have existing ties
(Reding, Sigmon, Jafri, Smith-Walker, Meyer, 2017). This clustering of relationships within
sectors may also be due to a network phenomenon known as homophily, where actors tend to
have ties with others that share certain commonalities such as attitudes, experiences, and
education (McPherson, Smith-Lovin, Cook, 2001). Homophily has been shown to facilitate
relationships through ease of communication because of these commonalities (McPherson, et al.,
2001). Networks that are examined through a relational SC lens will result in varying degrees of
ties because of the intent of the surveys. This study investigated formal and informal networks,
since survey questions aimed to determine the nature of the existing relationships present in the
OSE. The informal network represents the ties that were present in the network based on non-
prescriptive and non-professional ties. Informal networks highlight the presence of loose ties.
Further, informal ties can be used as the basis of developing and promoting new collaborations
(The Connected Company, n.d.).
The formal network in this study represents the ties that were present in the network based on a
history of collaboration. The identification and examination of these formal networks is
important because they have been shown to increase the probability of future collaborations and
innovation (Zach & Hill, 2017). Based on the information thus far, we present our first
hypotheses:
H1. Within the informal network, the relational SC of 4+ year organizations (UNO) will be
lower for intra-sector ties rather than inter-sector ties through the demonstration of network
density.
H2. Within the formal network, the relational SC of 4+ year organizations (UNO) will be greater
for inter-sector ties rather than intra-sector ties through the demonstration of network density.
While relational SC has been shown to increase the probability of future collaborations and
innovation, one study has shown that the structural SC of an actor is a better measure of whether
they will be involved in future collaborations (Zach & Hill, 2017). The importance of the
structural SC in predicting and managing collaboration necessitates its inclusion in this study.
Structural SC refers to how the patterns of ties determine the amount of SC present, not only to
individual actors within the network, but to the network as a whole. Metrics within structural SC
studies involve examining the positional centrality of the actors within the network. While there
are numerous types of centrality metrics, two of the most common are degree and betweenness.
Degree centrality is a measure of the number of ties an actor possesses as compared to the
number of ties possible within the boundaries of the network. A high degree centrality indicates
actors that have an increased awareness of others’ knowledge, which allows for easier
knowledge transfer (Brandon & Hollingshead, 2004; Nahapiet & Ghoshal, 1998). These
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 7
members are often called hubs and increase the scale of adoption of new innovations by passing
on new knowledge to many actors (Goldenberg, et al., 2009; Rogers, 2010).
The other centrality metric, betweenness, looks at how often an actor acts as a link between two
otherwise unconnected actors within the boundaries of the network. High betweenness indicates
an actor that is able to moderate the flow of knowledge, has access to relatively new knowledge,
is positively correlated with innovation, and increases the rate of innovations (Liu, Sidhu,
Beacom, & Valente, 2017; Rogers, 2010; Shaw-Ching, Madhavan & Sudharshan, 2005). These
actors are known as bridges or gatekeepers. It has been suggested that there are actors within
networks that can hold positions of both hubs (high degree centrality) and gatekeepers (high
betweenness centrality) (Guimarães et al., 2013). A previous study used a new methodology
involving the determination of actors who act as both hubs and gatekeepers to determine
emergent teacher leaders within a computer science education cohort (Reding, Dorn,
Grandgenett, Siy, Youn, Zhu, & Engelmann, 2016). This current study adapted that methodology
to determine which organization types within the OSE act as both gatekeepers and hubs by
calculating a combined centrality score.
Historical network data used by the OSE shows that, on average, the organization types in the
Academic stakeholder category tend to have lower numbers of loose ties than the organization
types in the other categories. The same data show that, on average, the organization types in the
Academic stakeholder category tend to have higher numbers of formal ties than the organization
types in the other categories (Reding, et al., 2017). Through the possession of these different
types of ties, the centrality scores of each organization type, including the 4+ year organization
type (UNO), will be different in the different networks (i.e., formal and informal).
This brings us to our final hypotheses:
H3. Within the informal network, the 4+ year organization type (UNO) will possess a relatively
low structural SC through the demonstration of one of the lowest combined centrality scores.
H4. Within the formal network, the 4+ year organization type (UNO) will possess a relatively
high structural SC through the demonstration of one of the highest combined centrality scores.
Methodology
The purpose of this study was to develop a methodology to determine the position of an urban
university within a MSCN. A document analysis of a digital survey administered via emailing an
OSE listserv was utilized. SNA methods were used in this study. Eighty-six individuals who
elected to complete the online survey comprise the population of this study. The respondents
were asked to report the level of interaction they had experienced with members of various
organization types within the OSE.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 8
DataCollection
The first step in the data collection process was to assign an identification number to each
respondent. The next step was to codify the various categorical factors of organization type,
stakeholder category, asset type, and interaction level. Each organization received a nominal
value and the possible organization types the respondents could select included 2 Year College,
4+ Year College, Business with less than 50 employees, Business with 51-200 employees,
Business with more than 200 employees, Career or Technical Training, Charitable Foundation,
Civic Organization, Faith Based Organization, Parent/Neighborhood Organization, Private P-12
Education, Public P-12 Education, Science Centers and/or Museums and Libraries, Youth
Serving Organizations, Military, Government, and Other. Next, a nominal identifier based on the
three stakeholder categories of Academic, Business, or Nonprofit was assigned. The nominal
values and identifiers (ID) for the organization type can be found in Table 1.
TABLE 1 . NominalIDandabbreviationbyorganizationtype
Organization Type Abbreviation Nominal ID
Academic Sector
2 Year College 2yr 1
4+ Year College 4yr 2
Career and/or Technical Training CTT 3
Private P-12 Education PrvP12 4
Public P-12 Education PubP12 5
Business Sector
Business with less than 50 Employees <50 6
Business with 51-200 Employees 51-200 7
Business with more than 200 Employees >200 8
Nonprofit Sector
Charitable Foundation CF 9
Civic Organization CO 10
Faith-based Organization FBO 11
Parent/Neighborhood Organization PNO 12
Science Centers/Museums/Libraries SCML 13
Youth Serving Organization YSO 14
Military MIL 15
Government GOV 16
Other 17
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 9
Next, the interaction levels were assigned ordinal values of “0,” “1,” or “2.” The ordinal values
of the interaction levels can be found in Table 2.
TABLE 2 . OrdinalIDbyinteractionlevel
Relationship Interaction Level Ordinal
ID
I am not familiar with anyone in this category No Interaction 0
I am familiar with someone in this category but we
did not work together in a professional manner Informal Interaction 1
We shared professional advice and/or materials when
opportunity arose Informal Interaction 1
We worked side-by-side as separate organizations but
did not have a formal agreement Formal Interaction 2
We worked together as a formal team with an
established formal agreement (such as a memorandum
of agreement)
Formal Interaction 2
DeterminingRelationalSocialCapital
Once interaction level frequencies were determined, an adjacency matrix for both the informal
and formal relationship levels were generated. These matrices were then used to determine
density, which represented the relational SC of each organization type based on the interaction
levels that were previously determined. Density is a measure of the number of ties that are
reported as compared to the number of possible ties that could exist. The intra-sector density and
inter-sector density for the informal and formal networks were calculated for each organization
type (refer to Table 1 for organization types and their sectors). The intra-sector density for the
informal network for the Academic organizations was calculated based on the number of
possible ties present within the Academic sector which included the organization types of 2-year
College, 4+ year College, Private P-12 Education, and Public P-12 Education (Career and
Technical Training were not included because there were no respondents from this organization
type). For example, if 4+ year Colleges reported informal ties with other 4+ year Colleges and
Private P-12 Education, but not with 2-Year Colleges and Public P-12 Education, then the
informal network intra-sector density for 4+ year Colleges would be 2 out of 4, or a density of
0.50. The inter-sector density for both networks for the Academic organizations were calculated
based on the number of possible ties within the Business and Nonprofit sectors which included
the organization types of Business with less than 50 employees, Business with 51-200
employees, Business with more than 200 employees, Charitable Foundations, Civic
Organizations, Science Centers/Museums/Libraries, and Youth Serving Organizations (Faith
Based organizations, Parent/Neighborhood organizations, Military, and Government
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 10
organizations were not included because of the absence of respondents from those organization
types). For example, if 4+ year Colleges reported informal ties with Business with more than 200
employees, Charitable Foundations and Science Centers/Museums/Libraries, but not with the
other Business and Nonprofit organization types, then the formal network inter-sector density for
4+ year Colleges would be 3 out of 8 or a density of 0.38.
DeterminingStructuralSocialCapital
The metrics of degree and betweenness centrality of each of the organization types represented
by the respondents of the survey were utilized to measure structural SC. Degree centrality is a
measure of the number of ties an actor has in the network and betweenness centrality is a metric
based on how well an actor is connected to otherwise unconnected actors and holds an
intermediary position between them. Using the adjacency matrix for the informal network, the
degree centrality and betweenness centrality metrics were calculated using NodeXL (Smith,
Ceni, Milic-Frayling, Shneiderman, Mendes Rodrigues, Leskovec, & Dunne, 2010). The metrics
for each individual was then converted to a scoring system where the actor with the highest
degree metric was given a degree score of 1, the actor with the next highest degree metric was
given a degree score of 2, and so on. This scoring conversion was also completed for
betweenness centrality metrics. Once each actor had a degree score and betweenness score, they
were added together to determine the combined centrality score. Since the study utilized a small
population, there were repeat degree centrality metrics. Ultimately, this study employed a
scoring conversion and not a ranking system; therefore, these ties were conserved and several
organization types received the same degree score, and then the scoring sequence continued with
the next number. This process was then repeated for the formal network using Level 2
interactions rather than Level 1 interactions.
ResultsandAnalysis
PopulationDemographics
There were 86 total surveys used in this analysis and respondents self-selected organization types
to which they belonged. Figure 1 represents the number of respondents per organization type and
Figure 2 represents the percentage of each stakeholder category represented by the respondents.
The majority of respondents belonged to the Academic stakeholder category at 50% with the 4+
Year College organization type, having the most respondents at 27. The number of respondents
in the 4+ year College organization type alone makes up 1/3 of the respondents of the entire
survey. This large number of representatives from this organization type shows a strong
engagement level from UNO.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 11
The second most represented stakeholder category was Nonprofit, with 33% of the respondents.
Within this category, Youth Serving Organizations had the most respondents with 10. The
Business stakeholder category had the third largest representation with 15% of the participants
who elected to complete the survey. Within this category, businesses with less than 50
employees represented the largest organization type with 8 respondents. There were no
representatives who completed the survey within the following categories: Career or Technical
Training, Parent/Neighborhood Organizations, Military, and Government.
FIGURE1.Percentageofrespondentsperorganizationtype
FIGURE2.Percentageofrespondentsperstakeholdercategory
2
27
02
12
7
421
8
10
810
002
0
5
10
15
20
25
30
Number of Respondents
Organization Type
Number of Respondents per Organization Type
Academic
50%
Business
15%
Nonprofit
33%
Other
2%
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 12
RelationalSocialCapital:Density
In order to determine relational SC, an adjacency matrix was initially developed that identified
which ties were present. The data were used to identify formal and informal networks within the
OSE based on the level of interaction actor one reported to have with actor two. Table 3 shows
the adjacency matrix for the informal network where actor one is represented by the organization
types in column 1 and actor 2 is represented by the organization types in row 1. When examining
4+ year Colleges (represented by 4yr), on average, they reported informal interactions with the
following organization types: Two Year Institutions, Private P-12 Schools, Businesses with less
than 50 Employees, Charitable Foundations, Civic Organizations, Faith Based Organizations,
Science Centers/Museums/Libraries, and Youth Serving Organizations.
TABLE 3 . Informalnetworkadjacencymatrix
OSE Organization Types
2yr 4yr Prv
P12
Pub
P12
<50 51-
200
>
200
CF CO FBO SCML YSO
2yr 0 0 0 0 1 0 1 0 1 1 1 1
4yr 1 0 1 0 1 0 0 1 1 1 1 1
PrvP12 1 0 1 0 1 1 1 1 1 1 0 1
PubP12 1 0 0 1 1 1 1 0 1 1 1 1
<50 1 1 1 0 0 1 1 1 1 1 1 0
51-200 1 1 1 1 1 0 0 1 0 1 1 1
>200 1 1 1 0 1 1 1 1 1 1 0 0
CF 1 1 1 1 1 1 1 1 1 0 0 0
CO 1 0 0 1 1 1 1 1 1 1 1 1
FBO 1 0 0 0 1 0 0 0 0 0 1 0
SCML 1 0 0 0 0 1 1 0 0 1 0 0
YSO 0 0 0 1 0 1 0 0 0 1 0 0
Figure 3 is the sociogram of the informal network and was produced through the Excel add-in
NodeXL (Smith, et al., 2010). The actors (OSE organization types) were represented as the
nodes and the reported relationships were the arcs (arrows). It should be noted that this is a
directed graph, meaning that the arrows represent the “direction” of the interaction from actor 1
to actor 2. This means that there are unreciprocated ties represented in the graph, for example,
there is a tie present between 4yr and CO even though CO did not report an informal relationship
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 13
on average with the 4+ year Colleges organization type. The organization types’ nodes were
represented by certain shapes and colors: Academic stakeholder category nodes were red
squares; Business stakeholder category nodes were green circles; and Nonprofit stakeholder
category nodes were blue triangles. This represented the organization types and their relative
positions to one another based on their reported relationships. Refer to Table 1 for a list of the
organization type abbreviations. There were no participants within the organization types of
Career or Technical Training, Parent/Neighborhood Organizations, and Military, thus they are
absent from the sociogram. The organization type of “Other” is also absent since it is a broad
category for any respondents who felt they did not belong to any of the organization types listed.
FIGURE3.Sociogramofinformalnetwork
Table 4 shows the adjacency matrix for the formal network where actor one is represented by the
organization types in column 1 and actor 2 is represented by the organization types in row 1.
When examining 4+ year Colleges (represented by 4yr), on average, they reported formal
interactions with the following organization types: 4+ year Colleges and Public P-12 Schools.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 14
TABLE 4 .Formalnetworkadjacencymatrix
OSE Organization Types
2yr 4yr Prv
P12
Pub
P12 <50 51-
200
>
200 CF CO FBO SCML YSO
2yr 1 1 1 1 0 1 0 1 0 0 0 0
4yr 0 1 0 1 0 0 0 0 0 0 0 0
PrvP12 0 1 0 1 0 0 0 0 0 0 1 0
PubP12 0 1 1 0 0 0 0 1 0 0 0 0
<50 0 0 0 1 1 0 0 0 0 0 0 1
51-200 0 0 0 0 0 1 1 0 1 0 0 0
>200 0 0 0 0 0 0 0 0 0 0 0 0
CF 0 0 0 0 0 0 0 0 0 0 1 1
CO 0 1 1 0 0 0 0 0 0 0 0 0
FBO 0 1 1 1 0 0 0 1 1 1 0 1
SCML 0 1 1 1 1 0 0 1 1 0 1 1
YSO 1 1 1 0 1 0 0 1 1 0 1 1
Figure 4 shows the sociogram of the informal network and was produced through the Excel add-
in NodeXL (Smith, et al., 2010) and follows the same parameters as previously described for the
sociogram in Figure 3.
FIGURE4.Sociogramofformalnetwork
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 15
Once the interactions were identified, the relational SC of the organization types were calculated
using NodeXL (Smith, et al., 2010) and were determined by the density of each organization
type’s intra-sector and inter-sector ties for both network types, informal and formal (refer to
Table 1 for each organization type’s sector). The density of an actor’s network is the degree to
which the possible number of ties actually occur. If all possible ties occurred, the density would
be 1, conversely, if none of the possible ties occurred, the density would be 0. This data is
represented in Table 5.
TABLE 5 . Intra‐sectorandinter‐sectordensitybyorganizationtype
Organization
Type
Intra-sector
Informal
Network Density
Inter-sector
Informal
Network Density
Intra-sector
Formal Network
Density
Inter-sector
Formal Network
Density
2 Yr 0.00 0.75 1.00 0.25
4+ Yr 0.50 0.75 0.50 0.00
Prv P12 0.50 0.88 0.50 0.12
Pub P12 0.50 0.88 0.50 0.12
Bus <50 0.67 0.78 0.33 0.22
Bus 51-200 0.33 0.78 0.67 0.11
Bus >200 1.00 0.67 0.00 0.00
CF 0.40 1.00 0.40 0.00
CO 1.00 0.71 0.00 0.29
FBO 0.20 0.29 0.80 0.43
SCML 0.20 0.43 0.80 0.57
YSO 0.20 0.29 0.80 0.57
H1. Within the informal network, the relational SC of 4+ year Colleges (UNO) is lower for intra-
sector ties rather than inter-sector ties through the demonstration of network density.
The intra-sector relational SC of 4+ year Colleges within the informal network was 0.50, which
means that 50% of the possible ties it could have with other Academic organizations were at the
informal level. The inter-sector relational SC of 4+ year Colleges was 0.75, which means that
75% of the possible ties it could have with Business and Nonprofit organizations were at the
informal level. This means that 4+ year Colleges possessed relatively more informal ties with
organization types outside of its sector than within its sector as predicted by H1.
In addition, when looking at the informal network intra- and inter-sector ties of the organization
types that belong to the Business sector, this pattern persisted. The organization types in the
Business sector all show a lower intra-sector relational SC compared to their inter-sector
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 16
relational SC in the informal network. This pattern also persisted for the nonprofit organization
types, except for Charitable Organizations.
H2: Within the formal network, the relational SC of 4+ year Colleges (UNO) is greater for inter-
sector ties rather than intra-sector ties through the demonstration of network density
The intra-sector relational SC of 4+ year Colleges within the formal network was 0.50, which
means that 50% of the possible ties it could have with other Academic organizations were at the
formal level. The inter-sector relational SC of 4+ year Colleges is 0.00, which means that 0% of
the possible ties it could have with Business and Nonprofit organizations were at the formal
level. This means that 4+ year Colleges possess relatively more formal ties with organization
types within its sector than without its sector, on average there were no formal ties reported
between individuals from 4+ year Colleges types and any Business or Nonprofit organization
types as predicted by H2.
In addition, when looking at the formal network intra- and inter-sector ties of the organization
types that belong to the Business sector, this pattern persisted. The organization types in the
Business sector all show a greater intra-sector relational SC compared to their inter-sector
relational SC in the formal network, except for the organization type of Businesses with greater
than 200 employees, which on average reported no intra- or inter-sector formal ties. This pattern
also persisted for the nonprofit organization types, except for Charitable Organizations.
In terms of collaboration, the data supported the notion that relational SC is based on the history
of interactions of the actors within a network and that on average, individuals from organization
types that belong to similar sectors, such as Academic, Business, and Nonprofit, tend to bring
with them formal ties with other individuals that belong to the same sector. The data also
supported the notion that the individuals within a network on average will have informal ties or
no ties to individuals who are in different sectors.
StructuralSocialCapital:CombinedCentralityScore
The metrics of degree and betweenness centrality of each of the organization types represented
by the respondents of the survey were utilized to measure structural SC. Degree centrality is a
measure of the number of ties an actor has in the network and betweenness centrality is a metric
based on how well an actor is connected to otherwise unconnected actors, and holds an
intermediary position between them.
H3: Within the informal network, the 4+ year Colleges type (UNO) will possess a relatively low
structural SC through the demonstration of one of the lowest combined centrality scores.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 17
The combined centrality scores were calculated using NodeXL (Smith, et al., 2010) based on the
degree and betweenness centralities for informal and formal networks, which are represented in
Table 6 and 7, respectively. The top organization types with the best combined centrality scores
for the informal network were Civic Organizations and Businesses with 51-200 Employees. This
means that, on average, these respondents had both a large number of ties within the informal
network as well as acted as intermediaries for otherwise unconnected organization types. The
bottom organization types with the worst combined centrality scores were Science Centers,
Museums, and Libraries, and Youth Serving Organizations.
TABLE 6 . Informalnetworkcentralitymetricsandscoresbyorganizationtype
Organization
Type
Degree
Centrality Degree Score Betweenness
Centrality
Betweenness
Score
Combined
Centrality
Score
2 Yr 0.50 4 1.266 1 5
4+ Yr 0.67 3 1.030 2 5
Prv P12 0.75 2 0.577 6 8
Pub P12 0.75 2 0.887 3 5
Bus <50 0.75 2 0.647 5 7
Bus 51-200 0.75 2 1.266 1 3
Bus >200 0.75 2 0.647 5 7
CF 0.75 2 0.236 7 9
CO 0.83 1 1.266 1 2
FBO 0.25 6 0.831 4 10
SCML 0.33 5 0.111 8 13
YSO 0.25 6 0.236 7 13
When focusing on UNO, the organization type of 4+ Year Colleges had a combined centrality
score of 5, which is the third highest score with a degree score of 3 and a betweenness score of 2
which contradicts H3. H3 was based on previous results presented from the OSE in 2016 and this
demonstrates the dynamic nature of the OSE and how responses can change annually. UNO has
a relatively high number of ties within the informal network, and these respondents can act as
intermediaries connecting other network members. Further, UNO is in a position to foster these
relationships and build upon them to generate more formal and lasting collaborations not only by
developing partnerships with itself, but also by connecting organization types that aren’t already
connected. This also means that UNO’s structural position allows it the possibility to somewhat
influence the adoption rate of innovations within the OSE, as well as influence the scale to which
an innovation is adopted through the informal ties it holds within the OSE.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 18
TABLE 7 . Formalnetworkcentralitymetricsandscoresbyorganizationtype
Organization
Type
Degree
Centrality Degree Score Betweenness
Centrality
Betweenness
Score
Combined
Centrality
Score
2 Yr 0.50 3 8.543 2 5
4+ Yr 0.17 5 1.210 8 13
Prv P12 0.25 4 1.210 8 12
Pub P12 0.25 4 3.467 5 9
Bus <50 0.25 4 0.143 10 14
Bus 51-200 0.25 4 10.250 1 5
Bus >200 0.00 6 0.00 11 17
CF 0.17 5 0.710 9 14
CO 0.17 5 8.167 3 8
FBO 0.58 2 1.126 7 9
SCML 0.67 1 3.126 6 7
YSO 0.67 1 5.050 4 5
H4: Within the formal network, the 4+ year Colleges type (UNO) will possess a relatively high
structural SC through the demonstration of one of the highest combined centrality scores.
In the formal networks, the top organizations were 2-year Colleges, Businesses with 51 - 200
employees, and Youth Serving Organizations each with a combined centrality score of 5. This
means that on average, these respondents had both a relatively large number of ties within the
formal network as well as acted as intermediaries for otherwise unconnected organization types.
The bottom organization types with the worst combined centrality scores were Businesses with
less than 50 employees, Businesses with more than 200 employees and Charitable Organizations.
When focusing on UNO, the organization type of 4+ year Colleges had a combined centrality
score of 13 which is one of the bottom 3 scores with a degree score of 5 and a betweenness score
of 8 which contradicts H4. Again, H4 was based on data from the OSE survey in 2016. This
demonstrates some of the limitations of this methodology, which will be further discussed in the
conclusion. This means that UNO had a relatively low number of ties within the formal network,
and these respondents typically cannot act as intermediaries connecting other network members.
This reiterates the findings from the informal network because respondents could only select one
interaction level, so if the majority of ties were informal, then the number of formal ties were
fewer. Therefore, while UNO has some formal ties, there is room for growth within their formal
network. Consequently, UNO’s structural position within the formal network does not allow it to
influence the scale and rate of adoption of innovations.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 19
Conclusion
The purpose of this study was to determine how universities could use SNA to determine their
structural position within MSCNs, as it is beneficial for an organization to identify their
structural positions and understand the sphere of their influence of the SC and innovation within
their network. Additionally, it is beneficial not only for an organization to identify and
understand their own position, but to also identify others’ positions within the network. This
study demonstrated that SNA can be used to determine the structural positions of organization
types within the OSE. Analysis was completed by averaging the interaction levels of the
respondents within the organization types to determine interaction levels. This information was
then used to calculate the centrality metrics of density as the metric for Relational SC and
combining degree and betweenness as the metric for Structural SC. After converting these
metrics to a scoring system and adding them, each organization type received a combined
centrality score that identified organization types that acted as both hubs and gatekeepers.
Limitations
One of the limitations of this study was that it was based on a self-reporting survey, which allows
for biases, namely social desirability bias and reference bias. Social desirability bias occurs when
respondents select options because they want to seem more “popular” (West, 2014), while
reference bias occurs when respondents interpret the various options differently (West, 2014).
The survey used in this study did attempt to minimize reference bias by providing explanation of
each interaction level. Desirability and reference biases affect all self-reporting surveys and
further research needs to be conducted to determine how to minimize these threats. The data
were also limited by the inconsistent spread of the number of respondents per organization type.
The frequencies for those organization types that had fewer respondents were heavily influenced
by only those few as compared to a larger number of responses.
This study was also limited by the network analysis, as it was based on averaging the interaction
levels of the individual members of the organization types and does not represent the whole
network. Also, it was not possible to represent the whole network, as surveys were completed by
individuals who elected to participate. Further, utilizing whole network analysis would have been
too time-intensive, as respondents would be required to select an interaction level for 86 different
individuals.
Discussion
Through studying the OSE, we were able to look at the differences in various forms of SC for
UNO within a growing, dynamic MSCN. Breaking the members of the network into different
sectors allowed us to further examine the network through intra- and inter-sector ties. We were
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 20
also able to disaggregate the ties into two different networks based on the strength of formal and
informal ties. In this study, we determined that the majority of UNO’s informal network ties
were with organization types outside of the Academic sector, meaning that respondents from
UNO had, on average, more informal ties with individuals from Business and Nonprofit
organization types than with Academic organization types. We also determined that the majority
of UNO’s formal network ties were with organization types inside of the Academic sector,
meaning that, on average, they had more formal ties with individuals from Academic
organization types than with Business or Nonprofit organization types. In fact, they only had two
formal ties in the network which included Four Year Institutions and P-12 Public Schools.
In terms of the structural SC, the results from this study did not support the proposed hypotheses.
UNO possessed a relatively high structural SC in the informal network and a relatively low
structural SC in the formal network, which directly oppose H3 and H4. UNO had the third
highest combined centrality score for the informal network, which means UNO is in a position to
foster these informal relationships and encourage more meaningful collaborations. However,
UNO had one of the bottom three combined centrality scores for the formal network. When
looking at the two networks from this perspective, it makes sense the UNO would have a low
score for the formal network because they held an influential position in the informal network
and respondents can only select one level of interaction. It also demonstrates room for growth for
UNO in terms of developing their informal relationships into formal relationships and helping to
facilitate this growth for others within the network. These interactions can be best fostered
through frequent, energetic, and genuine face-to-face interactions, resulting in increasingly more
meaningful and sustainable relationships (Pentland, 2015).
When combining the results of the Relational SC and Structural SC of UNO for both networks,
there are several organization types with which UNO needs to strategically form relationships.
On average, individuals from UNO recorded no relationships with individuals from Business
organization types with 51-200 Employees and > 200 Employees. In particular, it would be
beneficial for UNO to seek out partnerships with individuals from Businesses with 51-200
Employees as they are in structural positions of hubs and gatekeepers in the formal network and,
on average, UNO recorded no relationships with any individuals from this organization type.
Developing partnerships with individuals from the Businesses with 51-200 Employees would be
advantageous for UNO because these individuals have many formal connections and can help
UNO make new connections and be involved in formal collaborations.
UNO also has many informal relationships that they can build upon, particularly in the Nonprofit
sector; one organization type within the Nonprofit sector that UNO should enhance its
relationship with is Youth Serving Organizations. On average, individuals in these organization
types hold positions of prominence in the formal network, and individuals from UNO already
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 21
have informal relationships with these individuals, which can help facilitate a more formal
collaborative relationship.
Within research concerning cross-sector partnerships, SNA is being used to analyze and manage
these collaborations. The role of urban universities within these MSCNs can be informed and
enhanced through the use of SNA. Universities should strive to be prominent actors by holding
positions of hubs and gatekeepers within their innovative MSCNs, because while they hold
content expertise, having access to diverse perspectives is vital to the generation of creativity and
insight (Pentland, 2015). This creativity and insight will be beneficial for both the university and
the community as a whole.
Acknowledgements
The authors of this study would like to acknowledge the following people for their contributions
to this project: Drs. Neal Grandgenett, Elliott Ostler, and Sarah Edwards from UNO, Julie
Sigmon, director of the Omaha STEM Ecosystem, and Dr. Elizabeth Mulkerrin from the Omaha
Henry Doorly Zoo.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 22
References
Becker, J., & Smith, D. (2018). The Need for Cross-Sector Collaboration. Stanford Social
Innovation Review, 16(1), C2-C3. DOI: 10.1016/j.progress.2007.02.003
Benneworth, P., & Hospers, G. J. (2007). Urban competitiveness in the knowledge economy:
Universities as new planning animateurs. Progress in Planning, 67(2), 105-197.
DOI: 10.1016/j.progress.2007.02.003
Borgatti, S. (2006). Identifying sets of key players in a social network. Computational &
Mathematical Organization Theory, 12(1), 21-34. DOI:10.1007/s10588-006-7084-x
Borgatti, S.P. & Ofem, B. 2010, "Overview: Social network theory and analysis" in Social
network theory and educational change, ed. A.J. Daly, Cambridge, Mass.: Harvard Education
Press, pp. 17. ISBN-13: 978-1-934742-81-5
Brandon, D.P. & Hollingshead, A.B. 2004, Transactive Memory Systems in Organizations:
Matching Tasks, Expertise, and People. (2004). Organization Science, 15(6), 633-644.
DOI 10.1287/orsc. 1040.0069
The Connected Company: Finding Value In Informal Employee Networks - The One Brief.
(2016). The One Brief. Retrieved from https://theonebrief.com/the-connected-company-finding-
value-in-informal-employee-networks/
Etzkowitz, H., Webster, A., Gebhardt, C., & Terra, B. (2000). The future of the university and
the university of the future: Evolution of ivory tower to entrepreneurial paradigm. Research
Policy, 29(2), 313-330. DOI: 10.1016/S0048-7333(99)00069-4
Goldenberg, J., Han, S., Lehmann, D., & Hong, J. (2009). The Role of Hubs in the Adoption
Process. Journal of Marketing, 73(2), 1-13. DOI: 10.1509/jmkg.73.2.1
Guimarães, A., Vieira, A.B., Silva, A.P.C. & Ziviani, A. 2013, "Fast centrality-driven diffusion
in dynamic networks", Proceedings of the 22nd International Conference on World Wide Web
ACM, pp. 821.
Inkpen, A., & Tsang, E. (2005). Social capital, networks, and knowledge transfer. Academy Of
Management Review, 30(1), 146-165. DOI: 10.5465/AMR.2005.15281445
Kenis, P. & Oerlemans, L. 2007, "The Social Network Perspective: Understanding the Structure
of Cooperation" in Oxford Handbook of Inter-Organizational Relations, eds. S. Cropper, C.
Huxham, M. Ebers & P.S. Ring, Oxford University Press, Oxford, England, UK, pp. 289.
©TheAuthor2019.PublishedbytheCoalitionofUrbanandMetropolitanUniversities.www.cumuonline.org
MetropolitanUniversities|DOI10.18060/22604|December2,2019 23
Liu, W., Sidhu, A., Beacom, A.M. & Valente, T.W. 2017, "Social network theory", The
international encyclopedia of media effects, pp. 1-12. DOI:
10.1002/9781118783764.wbieme0092
McPherson, M., Smith-Lovin, L., & Cook, J. (2001). Birds of a Feather: Homophily in Social
Networks. Annual Review of Sociology, 27, 415-444.
Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational
Advantage. The Academy of Management Review, 23(2), 242-266. DOI:10.2307/259373
OSE Steering Committee 2017, Omaha STEM Ecosystem Strategic Plan.
Pentland, A. 2015, Social Physics: How social networks can make us smarter, Penguin, New
York, NY.
Reagans, R., & Mcevily, B. (2003). Network Structure and Knowledge Transfer: The Effects of
Cohesion and Range. Administrative Science Quarterly, 48(2), 240-267. DOI: 10.2307/3556658
Reding, T., Dorn, B., Grandgenett, N., Siy, H., Youn, J., Zhu, Q., & Engelmann, C. (2016).
Identification of the Emergent Leaders within a CSE Professional Development
Program. Proceedings of the 11th Workshop in Primary and Secondary Computing
Education, 13-15, 37-44. DOI: 10.1145/2978249.2978254
Reding, T.E, Sigmon, J., Jafri, J., Smith-Walker, N., & Meyer, E. (2017). Mapping
connectedness: social networks analysis and systems thinking in practice. Conference
Presentation at Kansas City, MO.
Rogers, E.M. (2010) Diffusion of innovations, Simon and Schuster, New York, NY.
Shaw-Ching Liu, B., Madhavan, R., & Sudharshan, D. (2005). DiffuNET: The impact of
network structure on diffusion of innovation. European Journal of Innovation Management,8(2),
240-262. DOI: 10.1108/14601060510594701
Spillane, J.P., Healey, K. & Kim, C.M. (2010) Leading and managing instruction: Formal and
informal aspects of the elementary school organization. Social network theory and educational
change, 129-158.
Vlaisavljevic, V., Cabello‐Medina, C., & Pérez‐Luño, A. (2016). Coping with Diversity in
Alliances for Innovation: The Role of Relational Social Capital and Knowledge
Codifiability. British Journal of Management, 27(2), 304-322. DOI: 10.1111/1467-8551.12155
Wasserman, S. & Faust, K. (1994) Social network analysis: Methods and applications,
Cambridge University Press.
West, M.R. (2014) The Limitations of Self-Report Measures of Non-cognitive Skills, The Brown
Center Chalkboard Series, no. 92.
